Eurostat R tools

This R package provides tools to access Eurostat database as part of the rOpenGov project.

For contact information and source code, see the github page

Available tools

• Installation
• Finding data
• Downloading data
  
• Replacing codes with labels
  
• Selecting and modifying data
  
• Visualization
  
  • Triangle plot
    
• Citing the package
  
• Acknowledgements
  
• Session info
  

Installation

Release version:

install.packages("eurostat")

Development version:

library(devtools)
install_github("ropengov/eurostat")

Overall, the eurostat package includes the following functions:

library(eurostat)
kable(as.data.frame(ls("package:eurostat")))

Finding data

Function get_eurostat_toc() downloads a table of contents of eurostat datasets. The values in column 'code' should be used to download a selected dataset.

# Load the package
library(eurostat)
library(rvest)

# Get Eurostat data listing
toc <- get_eurostat_toc()

# Check the first items
library(knitr)
kable(head(toc))

With search_eurostat() you can search the table of contents for particular patterns, e.g. all datasets related to passenger transport. The kable function to produces nice markdown output. Note that with the type argument of this function you could restrict the search to for instance datasets or tables.

# info about passengers
kable(head(search_eurostat("passenger transport")))

Codes for the dataset can be searched also from the Eurostat database. The Eurostat database gives codes in the Data Navigation Tree after every dataset in parenthesis.

Downloading data

Here an example of indicator Modal split of passenger transport. This is the percentage share of each mode of transport in total inland transport, expressed in passenger-kilometres (pkm) based on transport by passenger cars, buses and coaches, and trains. All data should be based on movements on national territory, regardless of the nationality of the vehicle. However, the data collection is not harmonized at the EU level.

Pick and print the id of the data set to download:

id <- search_eurostat("Modal split of passenger transport", 
                         type = "table")$code[1]
print(id)

[1] “tsdtr210”

Get the corersponding table. As the table is annual data, it is more convient to use a numeric time variable than use the default date format:

dat <- get_eurostat(id, time_format = "num")

Investigate the structure of the downloaded data set:

str(dat)

'data.frame': 2520 obs. of 5 variables: $ unit : Factor w/ 1 level “PC”: 1 1 1 1 1 1 1 1 1 1 … $ vehicle: Factor w/ 3 levels “BUS_TOT”,“CAR”,..: 1 1 1 1 1 1 1 1 1 1 … $ geo : Factor w/ 35 levels “AT”,“BE”,“BG”,..: 1 2 3 4 5 6 7 8 9 10 … $ time : num 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 … $ values : num 11 10.6 NA 3.7 NA NA 9.1 11.3 NA 32.4 …

kable(head(dat))

Replacing codes with labels

Eurostat variable IDs can be replaced with human-readable labels. Function label_eurostat() replaces the eurostat IDs based on definitions from Eurostat dictionaries.

datl <- label_eurostat(dat)
kable(head(datl))

Vehicle information has 3 levels. They are:

levels(datl$vehicle)

## [1] "Motor coaches, buses and trolley buses"
## [2] "Passenger cars"                        
## [3] "Trains"

You can get also labels for variable names

label_eurostat_vars(names(datl))

## Warning in label_eurostat(x, dic = "dimlst", lang = lang): All labels for
## dimlst were not found.

## [1] "Unit of measure"                                                                     
## [2] "Vehicles"                                                                            
## [3] "Geopolitical entity (reporting)"                                                     
## [4] "Period of time (a=annual, q=quarterly, m=monthly, d=daily, c=cumulated from January)"
## [5] NA

Selecting and modifying data

EFTA, Eurozone, EU and EU candidate countries

To facilititate fast plotting of standard European geographic areas, the package provides ready-made lists of the country codes used in the eurostat database for EFTA (efta_countries), Euro area (ea_countries), EU (eu_countries) and EU candidate countries (candidate_countries). This helps to select specific groups of countries for closer investigation. For conversions with other standard country coding systems, see the countrycode R package. To retrieve the country code list for EFTA, for instance, use:

data(efta_countries)
print(efta_countries)

##   code          name
## 1   IS       Iceland
## 2   LI Liechtenstein
## 3   NO        Norway
## 4   CH   Switzerland

EU data from 2012 in all vehicles:

dat_eu12 <- subset(datl, geo == "European Union (28 countries)" & time == 2012)
kable(dat_eu12, row.names = FALSE)

EU data from 2000 - 2012 with vehicle types as variables:

Reshaping the data is best done with spread() in tidyr.

library("tidyr")
dat_eu_0012 <- subset(dat, geo == "EU28" & time %in% 2000:2012)
dat_eu_0012_wide <- spread(dat_eu_0012, vehicle, values)
kable(subset(dat_eu_0012_wide, select = -geo), row.names = FALSE)

Train passengers for selected EU countries in 2000 - 2012

dat_trains <- subset(datl, geo %in% c("Austria", "Belgium", "Finland", "Sweden")
                     & time %in% 2000:2012 
                     & vehicle == "Trains")

dat_trains_wide <- spread(dat_trains, geo, values) 
kable(subset(dat_trains_wide, select = -vehicle), row.names = FALSE)

Visualization

Visualizing train passenger data with ggplot2:

library(ggplot2)
p <- ggplot(dat_trains, aes(x = time, y = values, colour = geo)) 
p <- p + geom_line()
print(p)

Triangle plot

Triangle plot on passenger transport distributions with 2012 data for all countries with data.

library(tidyr)

transports <- spread(subset(dat, time == 2012, select = c(geo, vehicle, values)), vehicle, values)

transports <- na.omit(transports)

# triangle plot
library(plotrix)
triax.plot(transports[, -1], show.grid = TRUE, 
           label.points = TRUE, point.labels = transports$geo, 
           pch = 19)

For further examples, see also the blog post on the eurostat R package.

Citing the package

Citing the Data Kindly cite Eurostat.

Citing the R tools This work can be freely used, modified and distributed under the BSD-2-clause (modified FreeBSD) license:

citation("eurostat")

## 
## Kindly cite the eurostat R package as follows:
## 
##   (C) Leo Lahti, Janne Huovari, Markus Kainu, Przemyslaw Biecek
##   2014-2016. eurostat R package URL:
##   https://github.com/rOpenGov/eurostat
## 
## A BibTeX entry for LaTeX users is
## 
##   @Misc{,
##     title = {eurostat R package},
##     author = {Leo Lahti and Janne Huovari and Markus Kainu and Przemyslaw Biecek},
##     year = {2014},
##     url = {https://github.com/rOpenGov/eurostat},
##   }

Acknowledgements

We are grateful to all contributors and Eurostat open data portal! This rOpenGov R package is based on earlier CRAN packages statfi and smarterpoland. The datamart and reurostat packages seem to develop related Eurostat tools but at the time of writing this tutorial this package seems to be in an experimental stage. The quandl package may also provides access to some versions of eurostat data sets.

Session info

This tutorial was created with

sessionInfo()

## R version 3.2.2 (2015-08-14)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 15.10
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] plotrix_3.6-1   ggplot2_2.0.0   tidyr_0.3.1     rvest_0.3.1    
## [5] xml2_0.1.2      eurostat_1.2.13 knitr_1.12     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.3      magrittr_1.5     munsell_0.4.2    colorspace_1.2-6
##  [5] R6_2.1.1         stringr_1.0.0    httr_1.0.0       highr_0.5.1     
##  [9] plyr_1.8.3       dplyr_0.4.3      tools_3.2.2      parallel_3.2.2  
## [13] grid_3.2.2       gtable_0.1.2     DBI_0.3.1        digest_0.6.9    
## [17] assertthat_0.1   formatR_1.2.1    evaluate_0.8     labeling_0.3    
## [21] stringi_1.0-1    scales_0.3.0